\Header{Normal}{The Normal Distribution}
\alias{dnorm}{Normal}
\alias{pnorm}{Normal}
\alias{qnorm}{Normal}
\alias{rnorm}{Normal}
\keyword{distribution}{Normal}
\begin{Description}\relax
Density, distribution function, quantile function and random
generation for the normal distribution with mean equal to \code{mean}
and standard deviation equal to \code{sd}.\end{Description}
\begin{Usage}
\begin{verbatim}
dnorm(x, mean=0, sd=1, log = FALSE)
pnorm(q, mean=0, sd=1, lower.tail = TRUE, log.p = FALSE)
qnorm(p, mean=0, sd=1, lower.tail = TRUE, log.p = FALSE)
rnorm(n, mean=0, sd=1)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{x,q}] vector of quantiles.
\item[\code{p}] vector of probabilities.
\item[\code{n}] number of observations. If \code{length(n) > 1}, the length
is taken to be the number required.
\item[\code{mean}] vector of means.
\item[\code{sd}] vector of standard deviations.
\item[\code{log, log.p}] logical; if TRUE, probabilities p are given as log(p).
\item[\code{lower.tail}] logical; if TRUE (default), probabilities are
\eqn{P[X \le x]}{P[X <= x]}, otherwise, \eqn{P[X > x]}{P[X > x]}.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
If \code{mean} or \code{sd} are not specified they assume the default
values of \code{0} and \code{1}, respectively.

The normal distribution has density
\deqn{
f(x) =
\frac{1}{\sqrt{2\pi}\sigma} e^{-(x-\mu)^2/2\sigma^2}}{f(x) = 1/(sqrt(2 pi) sigma) e^-((x - mu)^2/(2 sigma^2))
}
where \eqn{\mu}{mu} is the mean of the distribution and
\eqn{\sigma}{sigma} the standard deviation.

\code{qnorm} is based on Wichura's algorithm AS 241 which provides
precise results up to about 16 digits.\end{Details}
\begin{Value}
\code{dnorm} gives the density,
\code{pnorm} gives the distribution function,
\code{qnorm} gives the quantile function, and
\code{rnorm} generates random deviates.\end{Value}
\begin{References}\relax
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988)
\emph{The New S Language}.
Wadsworth \& Brooks/Cole.

Wichura, M. J. (1988)
Algorithm AS 241: The Percentage Points of the Normal Distribution.
\emph{Applied Statistics}, \bold{37}, 477--484.\end{References}
\begin{SeeAlso}\relax
\code{\Link{runif}} and \code{\Link{.Random.seed}} about random number
generation, and \code{\Link{dlnorm}} for the \emph{Log}normal distribution.\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
dnorm(0) == 1/ sqrt(2*pi)
dnorm(1) == exp(-1/2)/ sqrt(2*pi)
dnorm(1) == 1/ sqrt(2*pi*exp(1))

## Using "log = TRUE" for an extended range :
par(mfrow=c(2,1))
plot(function(x)dnorm(x, log=TRUE), -60, 50, main = "log { Normal density }")
curve(log(dnorm(x)), add=TRUE, col="red",lwd=2)
mtext("dnorm(x, log=TRUE)", adj=0); mtext("log(dnorm(x))", col="red", adj=1)

plot(function(x)pnorm(x, log=TRUE), -50, 10, main = "log { Normal Cumulative }")
curve(log(pnorm(x)), add=TRUE, col="red",lwd=2)
mtext("pnorm(x, log=TRUE)", adj=0); mtext("log(pnorm(x))", col="red", adj=1)

## if you want the so-called 'error function'
erf <- function(x) 2 * pnorm(x * sqrt(2)) - 1
## and the so-called 'complementary error function'
erfc <- function(x) 2 * pnorm(x * sqrt(2), lower=FALSE)
\end{ExampleCode}
\end{Examples}

